Supervised nonnegative matrix factorization model with fused correntropy for tumor recognition

Xiaoge Wei; Shengnan Liu; Lijun Yang

doi:10.1117/12.2681006

8 June 2023 Supervised nonnegative matrix factorization model with fused correntropy for tumor recognition

Xiaoge Wei, Shengnan Liu, Lijun Yang

Proceedings Volume 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023); 127073F (2023) https://doi.org/10.1117/12.2681006
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 2023, Changsha, China

Abstract

Nonnegative matrix factorization (NMF) is a feature learning method that can achieve nonlinear dimensional approximate reduction with strong interpretation, and it is widely used in the field of tumor recognition. The objective function of the traditional NMF model is based on the Euclidean distance metric, and the performance of the model is easily affected by the noise. Moreover, traditional NMF is an unsupervised feature learning method that does not use the label information of the data. However, it would cause a waste of information without using label information and cannot learn the discriminative features in the data. Therefore, the supervised nonnegative matrix factorization model with fused correntropy (FCSNMF) is proposed in this paper. The FCSNMF model alleviates the effect of noise in the experimental data by fusing the Euclidean distance metric and the maximum correntropy metric. In addition, the label consistency regularization term is skillfully chosen to utilize the label information of the data to obtain discriminative features. The effectiveness of the FCSNMF model is verified by applying it to a gene expression profile dataset for tumor recognition.

Citation Download Citation

Xiaoge Wei, Shengnan Liu, and Lijun Yang "Supervised nonnegative matrix factorization model with fused correntropy for tumor recognition", Proc. SPIE 12707, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2023), 127073F (8 June 2023); https://doi.org/10.1117/12.2681006

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